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Introduction to Intelligent Data Analysis
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[ms_accordion_item title=”Coordination” color=”” background_color=”” close_icon=”” open_icon=”” status=”open”]Cláudia Camila Dias 
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[ms_accordion style=”simple” type=”1″ class=”” id=””] [ms_accordion_item title=”Intended learning outcomes of the curricular unit” color=”#7b89b6″ background_color=”” close_icon=”” open_icon=”” status=”open”]
This unit aims to empower the students with theoretical foundations and practical approach to basic statistical methods used in clinical research, assessment of technologies and health service research. After this course unit the students should be able to aply the correct statistical methodology for data analysis using statistical software.and intrepret the results.
At the end of this course, the students should be able to characterize different types of data and variables; computerize and process the data ; check for errors; describe graphically data; describe data with summary measures; apply basic techniques of statistical inferences ( point and interval estimation and hypothesis tests) and to apply a simple linear regression model. Students should also be able to criticize the statistical analysis of scientific articles published in the literature in Health Sciences area, and identify opportunities for applying intelligent techniques in day-to-day practice of health data analysis.
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[ms_accordion style=”simple” type=”1″ class=”” id=””] [ms_accordion_item title=”Syllabus” color=”” background_color=”” close_icon=”” open_icon=”” status=”open”]
• Data Management: types of data and variables;
• Data processing;
• Error checking and inconsistencies;
• Descriptive statistics: measures of central tendency and dispertion;
• Graphicall representation of data;
• Introduction to probability;
• Normal distribution and other theoretical distributions;
• Statistical inference – Point estimation and confidence intervals;
• Fundamentals of Statistical inference – Sampling and estimation;
• Fundamentals of hypothesis testing;
• Parametric hypothesis tests: t test, F test (One-Way ANOVA);
• Non-parametric tests: Mann-Whintey, Kruskal-Wallis, Wilcoxon and qui-square tests;
• Measures of agreement;
• Basic methods of regression and correlation: simple and multiple linear regression;
• The role of artificial intelligence in health data analysis
• Opportunities and techniques for intelligent data analysis
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[ms_accordion_item title=”Demonstration of the syllabus coherence with the curricular unit’s intended learning outcomes” color=”” background_color=”” close_icon=”” open_icon=”” status=”open”]
The syllabus empowers students with necessary and sufficient concepts to understand, apply and integrate the methods adressed at real problems.
The syllabus will allow the student to acquire theoretical knowledge about data management, errors and inconsistencies, construction and interpretation of graphs, probability, statistical inference, hypothesis testing, compliance measures and regression and correlation techniques as well as develop practical skills to apply basic statistical analysis methods applied to clinical research objectives, technology assessment and research in health services, using appropriate statistical software.
These contents also fosters rthe development of the ability to critically analyse the statistical methods discussed in UC andto the interpretation of results.
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[ms_accordion_item title=”Teaching methodologies” color=”” background_color=”” close_icon=”” open_icon=”” status=”open”]
Teaching methodologies :
Presentation of each theoretical topic described for the course ;
Resolution of practical exercises;
Individual and group resolution of practical exercises ;
Group discussion of the exercises solved individually ;
Using an optimized platform for e -learning for teaching the topics taught in the course .
Evaluation methodology : Distributed evaluation with final exam . The evaluation will be conducted using practical exercises (50 %) and a final exam (50%).
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[ms_accordion_item title=”Demonstration of the coherence between the teaching methodologies and learning outcomes” color=”” background_color=”” close_icon=”” open_icon=”” status=”open”]
The presentation of theoretical topics allows students to know and understand the data management concepts, the construction and interpretation of graphs, the probability concept, the statistical inference, the hypothesis testing and the regression techniques. The exercices solving and the individual and group resolution of exercise provides insight to apply different methodologies to specific problems in the areas of clinical research and health services and technology assessment. Group discussions foster the development of critical spirit. The e-learning platform improves communication among students and between them and the teachers, and the provision of teaching materials.
The final exam evaluates the acquisition of theoretical concepts. The assessment through practical exercises evaluate the students’ ability to apply theoretical concepts to practical situations.
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[ms_accordion_item title=”Main bibliography” color=”” background_color=”” close_icon=”” open_icon=”” status=”open”]
Petrie, A. & Sabin, C. (2013). Medical Statistics at a Glance Workbook. West Sussex: Wiley-Blackwell. ISBN: 978-0-470-65848-2
Campbell M.J. & Swinscow T.D.V.(2009). Statistics at Square One (11th Edition), West Sussex: Wiley-Blackwell
Bland J.M. (2000). An Introduction to Medical Statistics (3rd edition). Oxford: Oxford Medical Publication
Berthold, M. & Hand, D. (2003). Intelligent Data Analysis – An Introduction. Berlin: Springer Verlag. ISBN: 978-3-540-48625-1
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